RESEARCH/R&D
Malaria is caused by parasites that are transmitted through the bites of infected mosquitoes. With about 200 million cases worldwide, and about 400,000 deaths per year, malaria is a major burden on global health. Most deaths occur among children in Africa, where malaria is a leading cause of childhood neuro-disability. Typical symptoms of malaria include fever, fatigue, headaches, and, in severe cases, seizures, coma, and death.
While existing drugs make malaria a curable disease, inadequate diagnostics and emerging drug resistance are major barriers to successful mortality reduction. The development of a fast and reliable diagnostic method is therefore one of the most promising ways of fighting malaria, together with better treatment, the development of new malaria vaccines, and mosquito control.
The current standard method for malaria diagnosis in the field is light microscopy of blood films. Millions of blood films are examined every year for malaria, which involves manual counting of parasites.
Accurate parasite counts are essential to diagnosing malaria correctly, testing for drug-resistance, measuring drug-effectiveness, and classifying disease severity. However, microscopic diagnostics is not standardized and depends heavily on the experience and skill of the microscopist. It is common for microscopists in low-resource settings to work in isolation, with no rigorous system in place that can ensure the maintenance of their skills and thus diagnostic quality. This leads to incorrect diagnostic decisions in the field. For false negative cases, this means unnecessary use of antibiotics, a second consultation, lost days of work, and in some cases progression into severe malaria. For false positive cases, a misdiagnosis entails unnecessary use of anti-malaria drugs and suffering from their potential side-effects, such as nausea, abdominal pain, diarrhea, and sometimes severe complications.
To improve malaria diagnostics, the Lister Hill National Center for Biomedical Communications, an R&D division of the US National Library of Medicine develops automated systems for parasite detection and counting in blood films for both human and rodent malaria.
Malaria Screener is an Android mobile application for automated malaria parasite counting in light microscopy. The mobile app utilizes the high-resolution cameras and the computing power of modern smartphones to screen both thin and thick blood smear images for P. falciparum parasites. Malaria Screener combines image acquisition, image analysis, and result visualization in its processing, and is equipped with a database for data management.
Malaria Screener GitHub Repository Access source code of Malaria Screener
Malaria Screener App Download Malaria Screener from the Google Play store
Extramural researchers:
Richard Maude, Kamolrat Silamut (Mahidol-Oxford Tropical Medicine Research Unit)
Ilker Ersoy, Kannappan Palaniappan (University of Missouri)
Rodent malaria models serve as essential preclinical antimalarial drug and vaccine testing tools. Evaluating these models requires manual counting of parasite-infected red blood cells, a time-consuming and repetitive process. Together with researchers at the Malaria Research Institute of Johns Hopkins University, we have developed machine learning software, Malaria Screener R, to expedite these studies by automating the counting of Plasmodium-infected red blood cells in rodents.
Malaria Screener R automates the tedious manual counting, requiring only a camera-equipped microscope. It features an intuitive graphical interface, aiding image processing and result visualization. Developed for offline use on Windows and Mac OS desktops, Malaria Screener R counts P. yoelii- and P. berghei-infected blood cells.
Download Malaria Screener R
Extramural researchers:
Sean Yanik, Prakash Srinivasan (Johns Hopkins School of Public Health).
To promote open science, we have made many of our datasets publicly available. These datasets contain thick and thin smear images of P. Falciparum and P. Vivax.
Please follow this link to see a full list of all sets available with download links:
Malaria Screener Datasets Details of datasets and download links
NLM: Feng Yang, Yasmin Kassim, Mahdieh Poostchi
NIAID: Abhisheka Bansal, Louis Miller, Susan Pierce, Joseph Brzostowski
Yu H, Mohammed FO, Hamid MA, Yang F, Kassim YM, Mohamed AO, Maude RJ, Ding XC, Owusu ED, Yerlikaya S, Dittrich S, Jaeger S. Patient-level performance evaluation of a smartphone-based malaria diagnostic application. Malar J 22, 33 (2023). https://doi.org/10.1186/s12936-023-04446-0.
Kassim YM, Yang F, Yu H, Maude RJ, Jaeger S. Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images. Diagnostics (Basel). 2021 Oct 27;11(11):1994. doi: 10.3390/diagnostics11111994. PMID: 34829341; PMCID: PMC8621537.
Kassim YM, Palaniappan K, Yang F, Poostchi M, Palaniappan N, Maude RJ, Antani S, Jaeger S. Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears. IEEE J Biomed Health Inform. 2021 May;25(5):1735-1746. doi: 10.1109/JBHI.2020.3034863. Epub 2021 May 11.
Yu H, Yang F, Rajaraman S, Ersoy I, Moallem G, Poostchi M, Palaniappan K, Antani S, Maude RJ, Jaeger S. Malaria Screener: a smartphone application for automated malaria screening. BMC Infect Dis. 2020 Nov 11;20(1):825. doi: 10.1186/s12879-020-05453-1.
Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S, Antani S. . Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J Biomed Health Inform. 2020 May;24(5):1427-1438. doi: 10.1109/JBHI.2019.2939121. Epub 2019 Sep 23.
Rajaraman S, Jaeger S, Antani SK. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ 7:e6977
Rajaraman S, Silamut K, Hossain MA, Ersoy I, Maude RJ, Jaeger S, Thoma GR, Antani SK. Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images. J Med Imaging (Bellingham). 2018 Jul;5(3):034501. doi: 10.1117/1.JMI.5.3.034501. Epub 2018 Jul 18.
Rajaraman S, Antani SK, Poostchi Mohammadabadi M, Silamut K, Hossain MA, Maude RJ, Jaeger S, Thoma GR. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ. 2018 Apr 16;6:e4568. doi: 10.7717/peerj.4568. eCollection 2018.
Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma GR. Image analysis and machine learning for detecting malaria. Transl Res. 2018 Apr;194:36-55. doi: 10.1016/j.trsl.2017.12.004. Epub 2018 Jan 12.